Existing methods can be split broadly into two categories that both operate in two stages:
Top-down methods [1]}, [2]} first extract room masks from the density map using neural networks
(e.g. , Mask R-CNN [3]}), then employ optimization/search techniques
(e.g. , integer programming [4]},
Monte-Carlo Tree-Search [5]}) to extract a polygonal floorplan.
Such techniques are not end-to-end trainable, and their success depends on how well the hand-crafted optimization captures domain knowledge about room shape and layout.
Alternatively, bottom-up methods [6]}, [7]} first detect corners, then look for edges between corners (i.e.  wall segments)
and finally assemble them into a planar floorplan graph.
Both approaches are strictly sequential and therefore dependent on the quality of the initial corner, respectively room, detector. The second stage starts from the detected entities, therefore missing or spurious detections may significantly impact the reconstruction.
